CN111829425B - Health monitoring method and system for civil aircraft leading edge position sensor - Google Patents
Health monitoring method and system for civil aircraft leading edge position sensor Download PDFInfo
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Abstract
The invention discloses a health monitoring method and a system of a civil aircraft leading edge position sensor, wherein the method comprises the following steps: acquiring historical parameters acquired by each leading edge position sensor; analyzing and processing the historical parameters acquired by each leading edge position sensor to extract an optimal parameter characteristic value capable of reflecting the health state of each leading edge position sensor; establishing a health monitoring model according to the optimal parameter characteristic value; acquiring a new parameter characteristic value acquired by a sensor, and inputting the new parameter characteristic value into a health monitoring model so as to calculate the health index of each corresponding leading edge position sensor through the health monitoring model; monitoring the health state of each leading edge position sensor in real time according to the health indexes; therefore, parameters acquired by the sensors during each flight are calculated through the health monitoring model so as to realize continuous monitoring and preventive maintenance of the health state of the front edge position sensor, and then maintenance cost and operation safety are greatly reduced.
Description
Technical Field
The invention relates to the technical field of civil aircraft leading edge position sensor monitoring, in particular to a health monitoring method of a civil aircraft leading edge position sensor, a computer readable storage medium, computer equipment and a health monitoring system of the civil aircraft leading edge position sensor.
Background
In the related art, the device of the leading edge of the civil aircraft wing comprises two Krueger flaps and four slats, wherein when the aircraft is cruising, the leading edge device is completely retracted, when the aircraft takes off, the leading edge device extends out to increase the lift force and allow the aircraft to roll at a lower speed, and when the aircraft lands, the leading edge slats completely extend out to increase the lift force and help the aircraft to prevent stalling; the leading edge flaps and slats have 30 proximity sensors for measurement to control the display of cockpit leading edge device signal panels and lights.
The leading edge position sensor is used as an important component of a leading edge indicating system, a direct signal capable of representing the health condition of the leading edge position sensor is not provided in the existing civil aircraft, only impedance data recorded by the leading edge position sensor is provided, and a considerable part of faults in the sensor are all expressed as sudden faults, so that the health monitoring of the leading edge position sensor is extremely difficult, the preventive maintenance cannot be carried out, the maintenance cost is greatly improved, and certain potential safety hazards exist.
Disclosure of Invention
The present invention is directed to solving at least one of the technical problems in the art to some extent. Therefore, one objective of the present invention is to provide a health monitoring method for a leading edge position sensor of a civil aircraft, which obtains an optimal parameter characteristic value by analyzing parameters acquired by the leading edge position sensor and establishes a health monitoring model, so that the parameters acquired by the sensor during each flight are calculated by the health monitoring model to realize continuous monitoring and preventive maintenance of the health state of the leading edge position sensor, thereby greatly reducing maintenance cost and operation safety.
A second object of the invention is to propose a computer-readable storage medium.
A third object of the invention is to propose a computer device.
A fourth objective of the present invention is to provide a road network extraction system.
In order to achieve the above object, a first embodiment of the present invention provides a health monitoring method for a leading edge position sensor of a civil aircraft, including the following steps: acquiring historical parameters acquired by each leading edge position sensor; analyzing and processing the historical parameters acquired by each leading edge position sensor to extract an optimal parameter characteristic value capable of reflecting the health state of each leading edge position sensor; establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each leading edge position sensor; acquiring a new parameter characteristic value acquired by each leading edge position sensor, and inputting the new parameter characteristic value acquired by each leading edge position sensor into the health monitoring model so as to calculate a health index of each corresponding leading edge position sensor through the health monitoring model; and monitoring the health state of each leading edge position sensor in real time according to the health index of each leading edge position sensor.
According to the health monitoring method of the civil aircraft leading edge position sensor, firstly, historical parameters acquired by each leading edge position sensor are acquired; then, analyzing and processing the historical parameters collected by each leading edge position sensor to extract the optimal parameter characteristic value which can reflect the health state of each leading edge position sensor; then, establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each front edge position sensor; then, acquiring a new parameter characteristic value acquired by each leading edge position sensor, and inputting the new parameter characteristic value acquired by each leading edge position sensor into a health monitoring model so as to calculate a corresponding health index of each leading edge position sensor through the health monitoring model; finally, the health state of each leading edge position sensor is monitored in real time according to the health index of each leading edge position sensor; therefore, parameters acquired by the front edge position sensor are analyzed to obtain the optimal parameter characteristic value and establish a health monitoring model, so that the parameters acquired by the sensor during each flight are calculated through the health monitoring model to realize continuous monitoring and preventive maintenance of the health state of the front edge position sensor, and further, the maintenance cost and the operation safety are greatly reduced.
In addition, the health monitoring method for the civil aircraft leading edge position sensor provided by the embodiment of the invention can also have the following additional technical characteristics:
optionally, the leading edge position sensors include a flap extension sensor, a flap retraction sensor, a slat outboard sensor, a slat inboard sensor, and a slat retraction sensor.
Optionally, the new parameter characteristic value acquired by each leading edge position sensor is input to the health monitoring model, so that the health index of each corresponding leading edge position sensor is calculated by the health monitoring model, specifically including: carrying out dimensionality reduction on the new parameter characteristic value acquired by the front edge position sensor by adopting a similar principal component analysis method; and performing sliding window processing on the data subjected to the dimensionality reduction by adopting a least square method to obtain a slope, and further acquiring a health index of the front edge position sensor.
Optionally, the similar principal component analysis method is used to perform dimensionality reduction on the new parameter characteristic value acquired by the leading edge position sensor, and the new parameter characteristic value is calculated by using the following formula:
wherein, X is input sample data which is matrix variable with dimension of n × m;
the model value of X, E is the error generated by establishing the model; l <m represents the number of pivot elements; t and P correspond to the score matrix and the load matrix,
is the covariance matrix of X, n is n in dimension n × m; i represents an identity matrix; xijIndividual fault dimensions representing xi, where xi ∈ Im×m；C＝I-PPT。
In order to achieve the above object, a second aspect of the present invention provides a computer-readable storage medium, on which a health monitoring program of a civil aircraft leading edge position sensor is stored, and when the health monitoring program of the civil aircraft leading edge position sensor is executed, the health monitoring program of the civil aircraft leading edge position sensor implements the steps in the health monitoring method of the civil aircraft leading edge position sensor.
According to the computer-readable storage medium of the embodiment of the invention, the health monitoring program of the civil aircraft leading edge position sensor is stored, so that the health monitoring method of the civil aircraft leading edge position sensor is realized by the processor when the health monitoring program of the civil aircraft leading edge position sensor is executed, therefore, the parameters acquired by the leading edge position sensor are analyzed to obtain the optimal parameter characteristic value and establish the health monitoring model, and the parameters acquired by the sensor during each flight are calculated by the health monitoring model to realize continuous monitoring and preventive maintenance of the health state of the leading edge position sensor, thereby greatly reducing the maintenance cost and the operation safety.
In order to achieve the above object, a third embodiment of the present invention provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the health monitoring method for a leading edge position sensor of a civil aircraft as described above.
According to the computer equipment provided by the embodiment of the invention, the computer program capable of running on the processor is stored through the memory, so that the processor can realize the health monitoring method of the civil aircraft leading edge position sensor when executing the computer program, therefore, the parameters acquired by the leading edge position sensor are analyzed to obtain the optimal parameter characteristic value and establish the health monitoring model, the parameters acquired by the sensor in each flight are calculated through the health monitoring model to realize continuous monitoring and preventive maintenance on the health state of the leading edge position sensor, and further, the maintenance cost and the operation safety are greatly reduced.
In order to achieve the above object, a fourth aspect of the present invention provides a health monitoring system for a leading edge position sensor of a civil aircraft, including: the acquisition unit is used for acquiring historical parameters acquired by each front edge position sensor; the sample preselection unit is used for analyzing and processing the historical parameters collected by each leading edge position sensor to extract an optimal parameter characteristic value capable of reflecting the health state of each leading edge position sensor; the model establishing unit is used for establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each front edge position sensor; and the model monitoring unit is used for acquiring a new parameter characteristic value acquired by each leading edge position sensor, inputting the new parameter characteristic value acquired by each leading edge position sensor into the health monitoring model so as to calculate a corresponding health index of each leading edge position sensor through the health monitoring model, and monitoring the health state of each leading edge position sensor in real time according to the health index of each leading edge position sensor.
The health monitoring system of the civil aircraft leading edge position sensor provided by the embodiment of the invention comprises: the acquisition unit is used for acquiring historical parameters acquired by each front edge position sensor; the system comprises a sample preselection unit, a parameter setting unit and a parameter setting unit, wherein the sample preselection unit is used for analyzing and processing historical parameters collected by each leading edge position sensor so as to extract an optimal parameter characteristic value capable of reflecting the health state of each leading edge position sensor; the model establishing unit is used for establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each front edge position sensor; the model monitoring unit is used for acquiring a new parameter characteristic value acquired by each leading edge position sensor, inputting the new parameter characteristic value acquired by each leading edge position sensor into the health monitoring model so as to calculate a corresponding health index of each leading edge position sensor through the health monitoring model, and monitoring the health state of each leading edge position sensor in real time according to the health index of each leading edge position sensor; therefore, parameters acquired by the front edge position sensor are analyzed to obtain the optimal parameter characteristic value and establish a health monitoring model, so that the parameters acquired by the sensor during each flight are calculated through the health monitoring model to realize continuous monitoring and preventive maintenance on the health state of the front edge position sensor, and further, the maintenance cost and the operation safety are greatly reduced.
In addition, the health monitoring system of the civil aircraft leading edge position sensor provided according to the embodiment of the invention can also have the following additional technical characteristics:
optionally, the leading edge position sensor includes a flap extension sensor, a flap retraction sensor, a slat outer side sensor, a slat inner side sensor, and a slat retraction sensor.
Optionally, the new parameter characteristic value acquired by each leading edge position sensor is input to the health monitoring model, so that the health index of each corresponding leading edge position sensor is calculated by the health monitoring model, which specifically includes: carrying out dimensionality reduction processing on the new parameter characteristic value acquired by the front edge position sensor by adopting a similar principal component analysis method; and performing sliding window processing on the data subjected to the dimensionality reduction by adopting a least square method to obtain a slope, and further acquiring a health index of the front edge position sensor.
Optionally, the similar principal component analysis method is used to perform dimensionality reduction on the new parameter characteristic value acquired by the leading edge position sensor, and the new parameter characteristic value is calculated by using the following formula:
wherein X is the inputThe sample data of (1), which is a matrix variable of dimension n × m;
the model value of X, E is the error generated by establishing the model; l <m represents the number of pivot elements; t and P correspond to a score matrix and a load matrix,
is a covariance matrix of X, n being n in the dimension n X m; i represents an identity matrix; xijIndividual fault dimensions representing xi, where xi ∈ Im×m；C＝I-PPT。
Drawings
FIG. 1 is a schematic flow chart of a health monitoring method for a civil aircraft leading edge position sensor according to an embodiment of the invention;
FIG. 2 is a parameter calculation for a leading edge position sensor of aircraft A according to one embodiment of the present invention;
FIG. 3 is a parameter calculation for a leading edge position sensor of aircraft B according to one embodiment of the present invention;
FIG. 4 is a block diagram of a health monitoring system for a civil aircraft leading edge position sensor in accordance with an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The existing civil aircraft does not provide a direct signal capable of representing the health condition of the civil aircraft, and only impedance data recorded by the front edge position sensor is provided, so that preventive maintenance cannot be carried out, the maintenance cost is greatly improved, and certain potential safety hazards exist; according to the health monitoring method of the civil aircraft leading edge position sensor, firstly, historical parameter characteristic values collected by each leading edge position sensor are obtained; then, analyzing and processing the historical parameter characteristic value acquired by each leading edge position sensor to extract the optimal parameter characteristic value which can reflect the health state of each leading edge position sensor; then, establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each front edge position sensor; then, acquiring a new parameter characteristic value acquired by each leading edge position sensor, and inputting the new parameter characteristic value acquired by each leading edge position sensor into a health monitoring model so as to calculate a corresponding health index of each leading edge position sensor through the health monitoring model; finally, the health state of each leading edge position sensor is monitored in real time according to the health index of each leading edge position sensor; therefore, parameters acquired by the front edge position sensor are analyzed to obtain the optimal parameter characteristic value and establish a health monitoring model, so that the parameters acquired by the sensor during each flight are calculated through the health monitoring model to realize continuous monitoring and preventive maintenance on the health state of the front edge position sensor, and further, the maintenance cost and the operation safety are greatly reduced.
In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
In order to better understand the technical solution, the technical solution will be described in detail with reference to the drawings and the specific embodiments.
Fig. 1 is a schematic flow chart of a health monitoring method of a civil aircraft leading edge position sensor according to an embodiment of the present invention, and as shown in fig. 1, the health monitoring method of the civil aircraft leading edge position sensor includes the following steps:
and S101, acquiring historical parameter characteristic values acquired by each front edge position sensor.
As one example, the leading edge position sensors include a flap extension sensor, a flap retraction sensor, a slat outboard sensor, a slat inboard sensor, and a slat retraction sensor, as shown in Table 1 below.
It should be noted that the historical parameter feature values are used for collecting the basic monitoring parameters for each leading edge position sensor, and the collected basic monitoring parameters of each leading edge position sensor are stored in the QAR and can be acquired in the QAR during analysis.
As a specific example, the leading edge position sensors include 1# -4 # flap extension sensors, 1# -4 # flap retraction sensors, 1# -8 # slat outboard sensors, 1# -8 # slat inboard sensors, and 2# -7 # slat retraction sensors, as shown in Table 1 below.
TABLE 1 parameter eigenvalue selection
S102, analyzing and processing the historical parameter characteristic value collected by each leading edge position sensor to extract the optimal parameter characteristic value capable of reflecting the health state of each leading edge position sensor.
It should be noted that the aircraft has different flight phases in a complete flight segment, and the working states of the leading edge flap and the slat in different flight phases are different, so that the parameter acquisition logic needs to be determined according to the parameter variation acquired by the corresponding sensors of the flap and the slat in different states.
As an example, the parameters of the flaps and slats during the extension, retraction and stable cruise phases, respectively, are extracted as optimal parameter characteristic values in the historical parameters.
As a specific example, as shown in table 1, the optimal parameter characteristic values collected by the flap extension sensor include an extension maximum value, a minimum value, an average value, a cruise phase stable value, and a variance; the optimal parameter characteristic values acquired by the flap retraction sensor comprise a retraction maximum value, a retraction minimum value, an average value, a cruise stage stable value and a variance; the optimal parameter characteristic values acquired by the slat outside sensor comprise an outside positive value average value and a negative value average value, and the average value is far away from the positive value average value and close to the negative value average value; the optimal parameter characteristic values acquired by the slat inside sensor comprise an inside positive value mean value and a negative value mean value, and the distance from the positive value mean value and the approach to the negative value mean value; the optimal parameter characteristic values acquired by the slat retraction sensor comprise a retraction maximum value, a retraction minimum value, an average value, a cruise stage stable value and a variance.
And S103, establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each front edge position sensor.
It should be noted that the health monitoring model is used for performing dimension reduction and sliding window processing on the input optimal parameter characteristic value, so as to obtain a historical parameter health slope value.
And S104, acquiring a new parameter characteristic value acquired by each leading edge position sensor, and inputting the new parameter characteristic value acquired by each leading edge position sensor into the health monitoring model so as to calculate the corresponding health index of each leading edge position sensor through the health monitoring model.
That is, when the aircraft performs a new flight mission, a new parameter characteristic value of the aircraft is acquired by each leading edge position sensor, and the corresponding new parameter characteristic value acquired by each leading edge position sensor is input to the health monitoring model, so that the health index of each leading edge position sensor is calculated by the health monitoring model.
As a specific embodiment, a similar principal component analysis method is adopted to perform dimension reduction processing on the new parameter characteristic value acquired by the leading edge position sensor; and performing sliding window processing on the data subjected to the dimensionality reduction by adopting a least square method to obtain a slope, and further acquiring a health index of the front edge position sensor.
And 105, monitoring the health state of each leading edge position sensor in real time according to the health index of each leading edge position sensor.
It should be noted that, when the aircraft executes a new flight, a new parameter is collected, and then the new parameter is calculated by the health monitoring model to obtain a new parameter slope value, that is, a health index of the leading edge position sensor, and the new parameter slope value is compared with a historical parameter health slope value, so as to determine the health condition of the corresponding sensor.
As a specific example, as shown in fig. 2, suppose that aircraft a performs 230 flight missions in total during a certain period; wherein, the front edge position sensor is executed to check the work card in a certain day, and the check result is normal; according to the modeling process, the QAR data of the airplane A in the period is decoded, and the parameters acquired by each flight segment are respectively obtained according to the table 2; then calculating to obtain an optimal parameter characteristic value corresponding to each parameter, and after obtaining each parameter characteristic value, firstly performing dimension reduction analysis on the optimal parameter characteristic value of each leading edge position sensor in order to reduce the model calculation complexity and improve the monitoring precision; the dimension reduction process comprises the following steps:
Assuming that X is a matrix variable of dimension n × m, X can be expressed by the following formula:
wherein, the first and the second end of the pipe are connected with each other,
a model value representing X, and E is the error resulting from modeling; the principal component analysis method can also be used for analyzing the principal component
And E is described by the following equation:
of these two formulae, l<m represents the number of the pivot elements, T and P correspond to a score matrix and a load matrix, and the score matrix and the load matrix are automatically calculated in matlab; the method comprises the following steps:
wherein the X matrix is decomposed in a manner to ensure
And
is an orthogonal matrix; the dimensionality reduction of the principal component analysis method reduces the initial m-dimensional variable to an l-dimensional principal component, and P is exactly the eigenvalue of the X covariance matrix.
Wherein the principal component l is obtained by adopting a method based on minimum reconstruction error variance, and u is setjIs the fault dimension xijThe above irreproducible variance is an important scale for judging the fault reconstruction; u. ofjThe smaller the value, the more excellent the reconstruction; u. ofjAnd xijThe relationship between them can be expressed by the following formula:
among the formulas, in the light of the equation,
is a covariance matrix of X, n is n in dimension n × m, I represents an identity matrix, C ═ I-PPT，ξjIndividual fault dimensions representing xi, where xi ∈ Im×m(ii) a From the formula (4), ujAssociated with the pivot, so that the optimum reconstruction is obtained by choosing the pivot number, by pairing u jMinimum processing is adopted to realize the optimal reconstruction in all dimensions, and a main body conforming to the following formula is selectedThe number of elements, resulting in an optimal reconstruction:
wherein m is the number of variables; so that the minimum is realized by selection
To obtain the best pivot. Thus dividing the input sample data into two parts:
corresponding to this embodiment: inputting 300 flight section acquisition parameters corresponding to the 1# flap in the X row of the sample set; the method comprises the following steps that different characteristic values of corresponding parameters of a column are processed in a centralized mode, covariance matrix calculation decomposition is carried out on input samples, corresponding characteristic vectors (w1, w2, a.. wn) corresponding to the largest n' characteristic values are taken out, and then the characteristic vectors are converted to obtain output samples D after dimension reduction; for example, according to the data characteristic values collected in table 1, there are 5 input characteristic values of the 1# flap and 1 output characteristic value after dimension reduction.
Furthermore, after the dimension reduction is performed on the data, the slope of the data after the dimension reduction is solved according to a sliding window, wherein the slope solving method adopts a least square method.
That is, firstly, the slope change ranges of the characteristic values of the 1# -8 # slats after dimension reduction are respectively obtained according to the dimension reduction and slope calculation by using data collected by a historical fault-free leading edge position sensor; and acquiring new parameters every time the aircraft executes a new flight, calculating the new parameters according to the health monitoring model to obtain new parameter slope values, and comparing the new parameter slope values with historical parameter health slope values to judge the health condition of the sensor.
It should be noted that the new parameters acquired by the aircraft executing the new flight are obtained by real-time delivery of the ACARS or decoding after flight, and then the parameters are transmitted to the health monitoring model, and the parameters obtained by transmission are calculated by the health monitoring model to obtain corresponding monitoring indexes.
As a specific embodiment, an airline maintenance engineer evaluates the health state of the leading edge position sensor according to the health index calculated by the health monitoring model, judges whether the sensor has a fault, and reasonably arranges maintenance tasks; and then entering the monitoring of the next period, thereby realizing the real-time monitoring of the leading edge position sensor of the fleet.
To further illustrate the present embodiment, fig. 2 shows aircraft a performing a total of 230 flights; the model calculation index of the 2# slat sensor fluctuates between 0.315 and 0.335 under the health condition, and the fluctuation range is small; model calculation indexes fluctuate between 0.31 and 0.34 under the health condition of the 7# slat sensor, and fluctuation range is small. As can be seen from the figure, the 7# slat sensor index of the airplane starts to fluctuate greatly around 180 flight cycles, and after being analyzed by engineers, the 7# slat sensor index is determined to continue to be monitored and pay attention to whether the 7# slat sensor index is further deteriorated; in subsequent continuous monitoring, the index of the 7# slat sensor is found to gradually deteriorate, and after 210 th flight cycle, the index can be observed to obviously deviate from a normal observation range; meanwhile, the indexes of the 2# slat on the symmetrical side with the 7# slat are in a normal variation range; the crew engineer then decides to maintain the 7# slat sensor after the voyage. After being detached, the measuring sensor is red-ground 7.85M omega; the blue-ground resistance value has jitter which is below 1M omega; yellow-earth 0.525 M.OMEGA. Wherein the FIM manual gives the measurement standard of the coil resistance value, wherein the red-yellow coil resistance value is required to be 24-38 ohms, and the blue-yellow coil resistance value is required to be 338-361 ohms; the resistance value of the sensor can be seen to be obviously overproof; and then the sensor is replaced, and the index tends to be normal after replacement.
For comparison, aircraft B performs a total of 280 flight missions, as shown in fig. 3. Firstly, decoding by using historical QAR data of an airplane B to obtain parameters required by modeling, then establishing a health monitoring model of the airplane B according to the model, and calculating index change conditions of 1# slats and 8# slats through the model; the model calculation index of the 1# slat sensor fluctuates between 0.285 and 0.305 under the health condition, and the fluctuation amplitude is small; model calculation indexes fluctuate between 0.295 and 0.310 under the health condition of the 8# slat sensor, and fluctuation range is small. It can be seen from the figure that the 1# slat sensor index of the airplane starts to fluctuate greatly around 205 flight cycles, the fluctuation is gradually increased, the index obviously deviates from a normal observation range, and meanwhile, the 8# slat index on the symmetrical side with the 1# slat is in a normal change range, which directly reflects that the severity of 1# sensor fault is continuously increased; then a real fault occurs in the 240 th flight cycle, and a cockpit effect occurs; determining the fault of a No. 1 slat sensor in post-aviation troubleshooting, wherein the insulation value of the sensor is only 7M omega; after the replacement and maintenance are carried out, the follow-up indexes gradually tend to be normal.
The analysis shows that if the airplane B is deployed and monitored, performance degradation of the sensor can be found out 20-30 flight cycles in advance, which means that the maintenance plan of the sensor can be arranged in advance, faults can be avoided, and preventive maintenance of the leading edge position sensor is achieved.
In conclusion, the modeling data of the front edge position sensor is obtained from QAR data acquired by the sensor, and the modeling method is a data-driven modeling method, does not need to master the complex structure and working principle of the sensor, and is more stable and reliable compared with the establishment of a physical model of the sensor; in addition, due to different geographical positions of many domestic aviation departments, the operating environmental conditions of the fleet are greatly different, and the health decline conditions of the sensors are completely different under the influence of the external factors; the method for modeling through the QAR data acquired by the sensor can reflect the health condition of the sensor more truly, so that the modeling method is more personalized.
By adopting a data mining method, through analyzing QAR data of a large number of airplane fleet historical sensor faults, key parameter characteristic values capable of reflecting the sensor faults are found out; and a modeling mode of single sensor independent analysis is adopted, so that the health monitoring of the sensor is more accurate and efficient.
The airline engineering engineer can visually know the health condition of the sensor at the front edge of the airplane; focusing continuous attention on sensors with abnormal health assessment, and reasonably arranging maintenance tasks according to assessment results and a production plan of a navigation driver; the real-time health monitoring and preventive maintenance of the front edge position sensor of the fleet are realized; flight delay, replacement and parking maintenance caused by unscheduled maintenance caused by the front edge position sensor are greatly reduced, the maintenance cost of the whole fleet is further reduced, and the operation safety is improved.
In addition, the embodiment of the invention also provides a computer readable storage medium, on which a health monitoring program of the civil aircraft leading edge position sensor is stored, and when the health monitoring program of the civil aircraft leading edge position sensor is executed, the steps in the health monitoring method of the civil aircraft leading edge position sensor are realized.
According to the computer-readable storage medium of the embodiment of the invention, the health monitoring program of the civil aircraft leading edge position sensor is stored, so that the processor can realize the health monitoring method of the civil aircraft leading edge position sensor when executing the health monitoring program of the civil aircraft leading edge position sensor, therefore, parameters acquired by the leading edge position sensor are analyzed to obtain the optimal parameter characteristic value and establish a health monitoring model, and the parameters acquired by the sensor during each flight are calculated through the health monitoring model to realize continuous monitoring and preventive maintenance of the health state of the leading edge position sensor, thereby greatly reducing the maintenance cost and the operation safety.
In addition, the embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the health monitoring method for a civil aircraft leading edge position sensor as described above.
According to the computer equipment provided by the embodiment of the invention, the computer program capable of running on the processor is stored through the memory, so that the processor can realize the health monitoring method of the civil aircraft leading edge position sensor when executing the computer program, therefore, the parameters acquired by the leading edge position sensor are analyzed to obtain the optimal parameter characteristic value and establish the health monitoring model, the parameters acquired by the sensor in each flight are calculated through the health monitoring model to realize the continuous monitoring and preventive maintenance of the health state of the leading edge position sensor, and further, the maintenance cost and the operation safety are greatly reduced.
Fig. 4 is a block diagram of a health monitoring system of a civil aircraft leading edge position sensor according to an embodiment of the present invention, and as shown in fig. 4, the health monitoring system of the civil aircraft leading edge position sensor of the present embodiment includes: an acquisition unit 201, a sample preselection unit 202, a model building unit 203, and a model monitoring unit 204.
The obtaining unit 201 is configured to obtain a historical parameter collected by each leading edge position sensor.
And the sample pre-selection unit 202 is used for analyzing and processing the historical parameters acquired by each leading edge position sensor to extract an optimal parameter characteristic value capable of reflecting the health state of each leading edge position sensor.
The model establishing unit 203 is used for establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each leading edge position sensor;
and the model monitoring unit 204 is configured to acquire a new parameter characteristic value acquired by each leading edge position sensor, input the new parameter characteristic value acquired by each leading edge position sensor into the health monitoring model, so as to calculate a health index of each corresponding leading edge position sensor through the health monitoring model, and monitor the health state of each leading edge position sensor in real time according to the health index of each leading edge position sensor.
As one example, the leading edge position sensors include a flap extension sensor, a flap retraction sensor, a slat outer side sensor, a slat inner side sensor, and a slat retraction sensor.
As an embodiment, the new parameter characteristic value acquired by each leading edge position sensor is input to the health monitoring model, so as to calculate the health index of each corresponding leading edge position sensor through the health monitoring model, which specifically includes: carrying out dimensionality reduction on the new parameter characteristic value acquired by the front edge position sensor by adopting a similar principal component analysis method; and performing sliding window processing on the data subjected to the dimensionality reduction by adopting a least square method to obtain a slope, and further acquiring a health index of the front edge position sensor.
As an embodiment, a similar principal component analysis method is used to perform dimensionality reduction on the new parameter characteristic value acquired by the leading edge position sensor, and the dimensionality reduction is specifically calculated by using the following formula:
wherein, X is input sample data which is a matrix variable with dimensions of n multiplied by m;
the model value of X, E is the error generated by establishing the model; l<m represents the number of pivot elements; t and P correspond to a score matrix and a load matrix,
is the covariance matrix of X, n is n in dimension n × m; i represents an identity matrix; xijIndividual fault dimensions representing xi, where xi ∈ Im×m；C＝I-PPT。
It should be noted that the foregoing explanations on the embodiment of the health monitoring method for the civil aircraft leading edge position sensor are also applicable to the health monitoring system for the civil aircraft leading edge position sensor of this embodiment, and are not repeated herein.
In summary, the health monitoring system of the civil aircraft leading edge position sensor according to the embodiment of the present invention includes: the acquisition unit is used for acquiring historical parameters acquired by each front edge position sensor; the system comprises a sample preselection unit, a parameter setting unit and a parameter setting unit, wherein the sample preselection unit is used for analyzing and processing historical parameters collected by each leading edge position sensor so as to extract an optimal parameter characteristic value capable of reflecting the health state of each leading edge position sensor; the model establishing unit is used for establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each front edge position sensor; the model monitoring unit is used for acquiring a new parameter characteristic value acquired by each leading edge position sensor, inputting the new parameter characteristic value acquired by each leading edge position sensor into the health monitoring model so as to calculate a corresponding health index of each leading edge position sensor through the health monitoring model, and monitoring the health state of each leading edge position sensor in real time according to the health index of each leading edge position sensor; therefore, parameters acquired by the front edge position sensor are analyzed to obtain the optimal parameter characteristic value and establish a health monitoring model, so that the parameters acquired by the sensor during each flight are calculated through the health monitoring model to continuously monitor the health state of the front edge position sensor and perform preventive maintenance, and further, the maintenance cost and the operation safety are greatly reduced.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
In the description of the present invention, it is to be understood that the terms "first", "second" and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above should not be understood to necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (6)
1. A health monitoring method of a civil aircraft leading edge position sensor is characterized by comprising the following steps:
acquiring historical parameters acquired by each leading edge position sensor;
analyzing and processing the historical parameters acquired by each leading edge position sensor to extract an optimal parameter characteristic value capable of reflecting the health state of each leading edge position sensor;
establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each leading edge position sensor;
acquiring a new parameter characteristic value acquired by each leading edge position sensor, and inputting the new parameter characteristic value acquired by each leading edge position sensor into the health monitoring model so as to calculate a health index of each corresponding leading edge position sensor through the health monitoring model;
monitoring the health state of each leading edge position sensor in real time according to the health index of each leading edge position sensor;
Wherein, inputting the new parameter characteristic value collected by each leading edge position sensor into the health monitoring model, so as to calculate the health index of each leading edge position sensor through the health monitoring model, specifically comprising:
carrying out dimensionality reduction on the new parameter characteristic value acquired by the front edge position sensor by adopting a similar principal component analysis method;
performing sliding window processing on the data subjected to the dimensionality reduction by adopting a least square method to obtain a slope, and further acquiring a health index of the front edge position sensor;
and (3) carrying out dimensionality reduction on the new parameter characteristic value acquired by the front edge position sensor by adopting a similar principal component analysis method, and specifically calculating by adopting the following formula:
wherein, X is input sample data which is matrix variable with dimension of n × m;
the model value of X, E is the error generated by establishing the model; l<m, the dimensionality reduction of the principal component analysis methodReducing the original m-dimensional variable into a l-dimensional principal element; t and P correspond to a score matrix and a load matrix,
is a covariance matrix of X, n being n in the dimension n X m; i represents an identity matrix; xijIndividual fault dimensions representing xi, where xi ∈ Im×m；C＝I-PPT，ujIndicating fault dimension xijIs not reconstructed.
2. The method of health monitoring of a civil aircraft leading edge position sensor of claim 1 wherein the leading edge position sensor includes a flap extension sensor, a flap retraction sensor, a slat outboard sensor, a slat inboard sensor and a slat retraction sensor.
3. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a health monitoring program for a civil aircraft leading edge position sensor, which health monitoring program, when executed, carries out the steps in the health monitoring method for a civil aircraft leading edge position sensor according to any one of claims 1 to 2.
4. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, implements the method of health monitoring of a civil aircraft leading edge position sensor according to any of claims 1-2.
5. A health monitoring system for civil aircraft leading edge position sensors, comprising:
the acquisition unit is used for acquiring historical parameters acquired by each front edge position sensor;
the sample preselection unit is used for analyzing and processing the historical parameters collected by each leading edge position sensor to extract an optimal parameter characteristic value capable of reflecting the health state of each leading edge position sensor;
The model establishing unit is used for establishing a health monitoring model according to the optimal parameter characteristic value corresponding to each front edge position sensor;
the model monitoring unit is used for acquiring a new parameter characteristic value acquired by each leading edge position sensor, inputting the new parameter characteristic value acquired by each leading edge position sensor into the health monitoring model so as to calculate a corresponding health index of each leading edge position sensor through the health monitoring model, and monitoring the health state of each leading edge position sensor in real time according to the health index of each leading edge position sensor;
wherein, inputting the new parameter characteristic value collected by each leading edge position sensor into the health monitoring model, so as to calculate the health index of each leading edge position sensor through the health monitoring model, specifically comprising:
carrying out dimensionality reduction on the new parameter characteristic value acquired by the front edge position sensor by adopting a similar principal component analysis method;
performing sliding window processing on the data subjected to the dimensionality reduction by adopting a least square method to obtain a slope, and further acquiring a health index of the front edge position sensor;
and (3) carrying out dimensionality reduction on the new parameter characteristic value acquired by the front edge position sensor by adopting a similar principal component analysis method, and specifically calculating by adopting the following formula:
Wherein, X is input sample data which is matrix variable with dimension of n × m;
the model value of X, E is the error generated by establishing the model; l. the<m, reducing the initial m-dimensional variable into l-dimensional pivot by the dimensionality reduction of the pivot analysis method; t and P correspond to a score matrix and a load matrix,
is a covariance matrix of X, n being n in the dimension n X m; i represents an identity matrix; xijIndividual fault dimensions representing xi, where xi ∈ Im×m；C＝I-PPT，ujIndicating fault dimension xijIs not reconstructed.
6. The health monitoring system for a civil aircraft leading edge position sensor of claim 5 wherein the leading edge position sensor includes a flap extension sensor, a flap retraction sensor, a slat outboard sensor, a slat inboard sensor and a slat retraction sensor.
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